Systematic Literature Review on Recommender System: Approach, Problem, Evaluation Techniques, Datasets

Abstract

Recommender systems are needed with the presence of the internet and social media. The benefits that are felt in the recommender system can make it easier for users to find suitable products and recommend other products, specifically with lots of information. Recommender systems continue to develop over time. This has led many researchers to continue to find the latest approach and evaluation techniques by comparing the performance of previously existing recommender systems. The main approaches that are often used in recommender systems are Content-Based Filtering (CBF), Collaborative Filtering (CF), and Hybrid Filtering (HBF). This time, we focus on conducting a Systematic Literature Review (SLR) of several research articles and analyzing methods for algorithms developed in building recommender systems. The SLR method consists of three stages: planning, implementation, and reporting. The research used as a comparison is between 2019-2023 using various existing data sets. There were 72 primary studies where the Collaborative Filtering method was used in 46 studies, Content-Based Filtering was used in 11 studies, and the Hybrid Filtering method was used in 15 studies. The results of this SLR process show the advantages and disadvantages of each method and type of evaluation developed in building a recommender system. Apart from that, several challenges arise with various existing problems. However, the model-based collaborative filtering method is one method that can minimize the problems of cold start, data sparsity, and scalability.

Educational Data Mining: Multiple Choice Question Classification in Vocational School

“…This stage evaluates the model by validating model performance with confusion matrix testing. Accuracy, precision, gain (also called sensitivity), and F Measure range from 0 to 1, and the calculation Equations are shown at (1), ( 2), ( 3) and ( 4) respectively [27,28].…”

“…The configuration compares 80:20 testing data to obtain training data of 6816 and testing data of 1704. After determining the division of the dataset [27], the data was randomized with parameters random state 42. The random seeds that divide the data into training and testing data are assigned with these parameters.…”